Method for ranking resources using node pool

ABSTRACT

An improved search engine creates correlations linking terms from inputs provided by a user to selected target terms. The correlation search process receives pre-processed inputs from a user including a wide variety of input formats including keywords, phrases, sentences, concepts, compound queries, complex queries and orthogonal queries. The pre-processing also includes pre-processing of general digital information objects and static or dynamic generation of questions. After a correlation search of the information presented by the pre-processing, the search results are processed in novel ways to provide an improved relevance ranking of results.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Ser. No. 11/426,932 filed Jun. 27,2006 now U.S. Pat. No. 8,140,559, which is a continuation-in-part of andclaims priority to U.S. Ser. No. 11/273,568, filed Nov. 14, 2005, nowU.S. Pat. No. 8,108,389 issued Jan. 31, 2012, and U.S. Ser. No.11/314,835, filed Dec. 21, 2005, now U.S. Pat. No. 8,126,890 issued Feb.28, 2012, and U.S. Ser. No. 60/694,331, filed Jun. 27, 2005, all ofwhich are hereby incorporated herein in their entireties by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention is related to information technology and, moreparticularly, to a search engine that utilizes the results of knowledgecorrelation to identify network and/or Internet resources significant toany given user question, subject, or topic of a digital informationobject.

2. Description of the Related Art

Search engines are widely acknowledged to be part of the InformationRetrieval (IR) domain of knowledge. IR methods are directed to locatingresources (typically documents) that are relevant to a question called aquery. That query can take forms ranging from a single search term to acomplex sentence composed in a natural language such as English. Thecollection of potential resources that are searched is called a corpus(body), and different techniques have been developed to search each typeof corpus. For example, techniques used to search the set of articlescontained in a digitized encyclopedia differ from the techniques used bya web search engine. Regardless of the techniques utilized, the coreissue in IR is relevance—that is, the relevance of the documentsretrieved to the original query. Formal metrics are applied to comparethe effectiveness of the various IR methods. Common IR effectivenessmetrics include precision, which is the proportion of relevant documentsretrieved to all retrieved documents; recall, which is the proportion ofrelevant documents retrieved to all relevant documents in the corpus;and fall-out, which is the proportion of irrelevant documents retrievedto all irrelevant documents in the corpus. Post retrieval, documentsdeemed relevant are (in most IR systems) assigned a relevance rank,again using a variety of techniques, and results are returned. Althoughmost commonly the query is submitted by—and the results returned to—ahuman being called a user, the user can be another software process.

Text retrieval is a type of IR that is typically concerned with locatingrelevant documents which are composed of text, and document retrieval isconcerned with locating specific fragments of text documents,particularly those documents composed of unstructured (or “free”) text.

The related knowledge domain of data retrieval differs from IR in thatdata retrieval is concerned with rapid, accurate retrieval of specificdata items, such as records from a SQL database.

Information extraction (IE) is another type of IR which is has thepurpose of automatic extraction of information from unstructured(usually text) documents into data structures such as a template ofname/value pairs. From such templates, the information can subsequentlycorrectly update or be inserted into a relational database.

Search engines that have been described in the literature or released assoftware products use a number of forms of input, ranging fromindividual keywords, to phrases, sentences, paragraphs, concepts anddata objects. Although the meanings of keyword, sentence, and paragraphconform to the common understanding of the terms, the meanings ofphrase, concept, and data object varies by implementation. Sometimes,the word phrase is defined using its traditional meaning in grammar. Inthis use, types of phrases include Prepositional Phrases (PP), NounPhrases (NP), Verb Phrases (VP), Adjective Phrases, and AdverbialPhrases. For other implementations, the word phrase may be defined asany proper name (for example “New York City”). Most definitions requirethat a phrase contain multiple words, although at least one definitionpermits even a single word to be considered a phrase. Some search engineimplementations utilize a lexicon (a pre-canned list) of phrases. TheWordNet Lexical Database is a common source of phrases.

When used in conjunction with search engines, the word concept generallyrefers to one of two constructs. The first construct is concept as acluster of related words, similar to a thesaurus, associated with akeyword. In a number of implementations, this cluster is made availableto a user—via a Graphic User Interface (GUI) for correction andcustomization. The user can tailor the cluster of words until theresulting concept is most representative of the user's understanding andintent. The second construct is concept as a localized semantic net ofrelated words around a keyword. Here, a local or public ontology andtaxonomy is consulted to create a semantic net around the keyword. Someimplementations of concept include images and other non-text elements.

Topics in general practice need to be identified or “detected” from aapplying a specific set of operations against a body of text. Differentmethodologies for identification and/or detection of topics have beendescribed in the literature. Use of a topic as input to a search enginetherefore usually means that a body of text is input, and a requiredtopic identification or topic detection function is invoked. Dependingupon the format and length of the resulting topic, an appropriaterelevancy function can then be invoked by the search engine.

Data objects as input to a search engine can take forms including avarying length set of free form sentences, to full-length textdocuments, to meta-data documents such as XML documents. The ObjectOriented (OO) paradigm dictates that OO systems accept objects asinputs. Some software function is almost always required to process theinput object so that the subsequent relevance function of the searchengine can proceed.

Ranked result sets have been the key to marketplace success for searchengines. The current dominance of the Google search engine (a product ofGoogle, Inc.) is due to far more to the PageRank system used in Googlethat lets (essentially) the popularity of a given document dictateresult rank. Popularity in the Google example applies to the number oflinks and to the preferences of Google users who input any given searchterm or phrase. These rankings permit Google to optimize searches byreturning only those documents with ranks above a certain threshold(called k). Other methods used by web search engines to rank resultsinclude “Hubs & Authorities” which counts links into and out of a givenweb page or document, Markov chains, and random walks.

BRIEF SUMMARY OF THE INVENTION

The present invention discloses a new and novel form of search enginewhich utilizes a computer implemented method to identify at least oneresource, referenced by that resource's unique URI (Uniform ResourceIdentifier) or referenced by that resource's URL (Uniform ResourceLocator), such resource being significant to any given user question,subject, or topic of a digital information object. For the presentinvention, the user question or subject or topic acts as input. Theinput is utilized by a software function which attempts to construct ordiscover logical structures within a collection of data objects, eachdata object being associated with the resource that contributed the dataobject, and the constructed or discovered logical structures beingstrongly associated with the input. For a preferred embodiment, thatsoftware function is a knowledge correlation function as described insaid Ser. No. 11/273,568 and the logical structure is a form of directedacyclic graph termed a quiver of paths. If such logical structuresstrongly associated with the input are in fact constructed ordiscovered, the data object members of such logical structures become ananswer space. Using the answer space, another software function is thenable to determine with a high degree of confidence which of theresources that contributed to the answer space are the most significantcontributors to the answer space, and thereby identify URLs and URIsmost significant to the input question, subject or topic. Finally, asoftware function is used to rank in significance to the input each ofthe URL and URI referenced resources that contributed data objects tothe answer space.

The present invention differs from existing search engines because theKnowledge Correlation process as described in said Ser. No. 11/273,568,which is used in this invention, attempts to construct an exhaustivecollection of paths describing all connections—calledcorrelations—between one term, phrase, or concept referred to as X (or“origin”) and a minimum of a second term, phrase or concept referred toas Y (or “destination”). If one or more such correlations can in fact beconstructed, the present invention identifies as relevant all resourceswhich contributed to constructing the correlation(s). Unlike existingsearch engines, relevancy in the present invention applies not toindividual team, phrases or concepts in isolation but instead to theanswer space of correlations that includes not only the X and the Y, butto all the terms, phrases and concepts encountered in constructing thecorrelations. Because of these novel characteristics, the presentinvention is uniquely capable of satisfying user queries for whichcannot be answered using the content of a single web page or document.

Input to the present invention differs from current uses because allinput modes of the present invention must present a minimum of two (2)non-identical terms, phrases, or concepts. “Non-identical” in this usagemeans lexical or semantic overlap or disjunction is required. Asdescribed in said Ser. No. 11/273,568, the minimum two terms, phrases,or concepts are referred to as X and Y (or “origin” and “destination”).No input process can result in synonymy, identity, or idempotent X and Yterm, phrases or concepts. As with existing art, text objects and dataobjects can be accepted (in the present invention, as either X or Y) andthe topics and/or concepts can be extracted prior to submission to theKnowledge Correlation process. However, unlike most (if not all)existing search engines, the form of the input (term, phrase, concept,or object) is not constrained in the present invention. This is possiblebecause the relevancy function (Knowledge Correlation) does not utilizesimilarity measures to establish relevancy. This characteristic willallow the present invention to be seamlessly integrated with manyexisting IR applications.

Regardless of the forms or methods of input, the purpose of KnowledgeCorrelation in the present invention is to establish document relevancy.Currently, relevancy is established in IR using three generalapproaches: set-theoretic models which represent documents by sets;algebraic models which represent documents as vectors or matrices; andprobabilistic models which use probabilistic theorems to learn documentattributes (such as topic). Each model provides a means of determiningif one or more documents are similar and thereby, relevant, to a giveninput. For example, the most basic set-theoretic model uses the standardBoolean approach to relevancy—does an input word appear in the document?If yes, the document is relevant. If no, then the document is notrelevant. Algebraic models utilize techniques such as vector spacemodels where documents represented as vectors of terms are compared tothe input query represented as a vector of terms. Similarity of thevectors implies relevancy of the documents. For probabilistic models,relevancy is determined by the compared probabilities of input anddocument.

As described above, the present invention establishes relevancy by anentirely different process, using an entirely different criteria thanany existing search engine. However, the present invention is dependentupon Discovery and Acquisition of “relevant” sources within the corpus(especially if that corpus is the WWW). For this reason, any form of theexisting art can be utilized without restriction during the Discoveryphase as described in said Ser. No. 11/273,568 to assist in identifyingcandidate resources for input to the Knowledge Correlation process.

For all search engines, simply determining relevancy of a given documentto a given input is necessary but not sufficient. After all—using thestandard Boolean approach to relevancy as an example—for any queryagainst the WWW, which contained the word “computer”, tens of millionsof documents would qualify as relevant. If the user was actuallyinterested only in documents describing a specific application of“computer”, such a large result set would prove unusable. As a practicalmatter, users require that search engines rank their results from mostrelevant to least relevant. Typically, users prefer to have the relevantdocuments presented in order of decreasing relevance—with the mostrelevant result first. Because most relevance functions produce realnumber values, a natural way to rank any search engine result set is torank the members of the result set by their respective relevance scores.

The present invention utilizes a ranking method that is novel because itis a function of the degree to which a given document or resourcecontributed to the correlation “answer space”. As described in said Ser.No. 11/273,568, that answer space is constructed from data structurescalled nodes, which in turn are created by decomposition of relevantresources. Even the most naïve ranking function of the presentinvention—which counts the frequency of node occurrence in the answerspace—can identify documents that uniquely or strongly relevant to theoriginal user query. More sophisticated ranking mechanisms of thecurrent invention as described more hereinafter improve that outcome.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing functional components of a searchengine in accordance with one aspect of the invention.

FIG. 2 is a clock diagram of the pre-search block of FIG. 1.

FIG. 2A is a block diagram of part of an exemplary subject evaluationfunction for keywords, phrases, sentences and concepts in accordancewith one aspect of the invention.

FIG. 2B is a block diagram of the remaining part of an exemplary subjectevaluation function for compound, complex or orthogonal subjects and fora simple web query in accordance with one aspect of the invention.

FIG. 2C is a block diagram of an exemplary topic detection module andrelated adapter(s) in accordance with one aspect of the invention.

FIG. 2D is a block diagram of a question generation function inaccordance with one aspect of the invention.

FIG. 3 is a copy of FIG. 1A of Ser. No. 11/273,568.

FIG. 4 is a copy of FIG. 1B of Ser. No. 11/273,568.

FIG. 5 is a copy of FIG. 1C of Ser. No. 11/273,568.

FIG. 6 is a copy of FIG. 2A of Ser. No. 11/273,568.

FIG. 7 is a copy of FIG. 2E of Ser. No. 11/273,568.

FIG. 8 is a block diagram of the post search block 120 of FIG. 1.

DESCRIPTION OF THE INVENTION

FIG. 1 is a block diagram of three examples of input accepted by thecorrelation function 110. A subject 200 is evaluated by the subjectevaluation function 220. A digital information object 230 is examinedfor a topic by an adapter 235 of the topic detection module 240. Acanonical form question generation function 250 generates a question 260as input.

In a preferred embodiment, a minimum of two inputs in any form and fromany source as described more hereinafter must be submitted to thecorrelation function 110. There is a first such input, called the X or“origin” input and there is a second such input, called the Y or“destination” input. Accordingly, acceptable inputs may include anycombination of two subjects 200, digital information objects 230, orquestions 260.

In another embodiment as described more hereinafter, a minimum of one Xinput which is a subject 200, digital information object 230 or question260 is submitted to the correlation function 110. A second input, calleda stop correlation condition, is passed to the correlation function 110.The actual value of the required Y which is a correlation destinationremains unknown until the stop correlation function is satisfied by thecorrelation function 110. No actual Y input need be processed as input,but the requirement for a correlation destination is satisfied.

The first example illustrated in FIG. 1 is illustrated in greater detailin FIG. 3. In one embodiment, the subject 200 may be an individualkeyword, a phrase, a sentence, or a concept. When the subject 200 is anindividual keyword, the subject 200 is passed by the subject evaluationfunction 220 directly to the correlation function 110 without furtherprocessing. Likewise, when the subject 200 is a phrase, the subject 200is passed by the subject evaluation function 220 directly to thecorrelation function 110 without further processing. When the subject200 is a sentence, a natural language parser (NLP) 133 will be invokedto perform a syntactic analysis of the sentence to extract the actualsubject 200 of the sentence in the form of words and/or phrases. Suchwords or phrases will then be passed to the correlation function 110.Additional words or phrases may be extracted from the sentence andsubmitted to the correlation function 110 as context. As described insaid Ser. No. 11/273,568, any number of context words or phrases whichare in addition to the X or Y words or phrases can be submitted to thecorrelation function 110 to improve said function. The selection of whatwords or phrases (if any) that are to be extracted from a sentence isbased upon the membership of the word or phrase in any lexicon of theNLP 133, and the absence of the word from a common list of stop words.Stop words are well known in IR. Such words cannot be used to establishrelevance in set-theoretic models of IR, so are never added to theindexes built for such models.

In the event that the sentence is a question 250 which matches acanonical form, the subject evaluation function 220 will extract fromthe sentence both the X and Y words or phrases and submit them to thecorrelation function 110. When the subject 200 is a concept, the conceptword or phrase will be submitted to the correlation function 110 aseither X or Y, and the remaining terms in the concept cluster or mapwill be submitted to the correlation function 110 as context words orphrases.

In a preferred embodiment, the subject will be provided by a user bymeans of Graphical User Interface such as FIG. 6. In other embodiments,any well known input interface will be utilized (e.g. text input field,spoken input, etc.).

In one embodiment, referring to FIG. 3, the subject 200 shall take theform of a complex subject, that is, a subject that consists of oneindependent clause, and one or more dependent clauses. For example,“regulation of pollution, given the effects of automobile pollution”. Inother embodiments, the subject 200 shall take the form of a compoundsubject, that is, a subject that consists of two or more independentclauses connected using logical operators such as “and” “or” “not”. Forexample, “the Trilateral Commission and international NGOs not WorldBank”. Alternatively, the subject 200 shall take the form of amulti-part orthogonal subject, that is, a subject that consists of twoor more independent clauses which are not connected, and which may beorthogonal with respect to each other. For example, “poaching,endangered species, men's health, government intervention”.Alternatively, the subject 200 shall take the form of a multi-partorthogonal subject, that is, a subject that consists of two or moreindependent clauses which are not connected, and which may be orthogonalwith respect to each other. For example, “poaching, endangered species,men's health, government intervention”. In these embodiments, advancedNLP methods for clause recognition (see Hachey, B. C. 2002. Thesis:Recognising Clauses Using Symbolic and Machine Learning Approaches.University of Edinburgh) will be applied to the subject 200 to firstdecompose the subject 200 into clauses and from there, by means ofsyntactic analysis, into keywords and phrases. Clause recognitiontechniques will be used to discriminate between X, Y, and context inputsto the correlation function 110.

In one embodiment, the subject evaluation function 220 will determine ifthe user-provided subject 200 would produce as response from the presentinvention a listing as the most appropriate response. For example,referring to FIG. 4, is the user-provided subject is “Italianrestaurants Dover DE”, the subject evaluation function 220 willrecognize that a listing of Italian restaurants in Dover, Del. issought. In this event, the subject evaluation function 220 will eitherdirect the user to use one of the well known simple web search enginessuch as Google (a product of Google, Inc.) or Yahoo (a product of Yahoo,Inc.), or will directly invoke one of those simple search engines.Alternatively, the subject evaluation function 220 will determine if theuser-provided subject would produce as response a single web page as themost appropriate response. For example, is the user-provided subject is“show times rialto theatre”, the subject evaluation function 220 willrecognize that the web site for the Rialto Theatre is sought. In thisevent, the subject evaluation function 220 will either direct the userto use one of the well known simple web search engines such as Google orYahoo, or will directly invoke the web site of Rialto Theatre, or willdirectly invoke one of the simple search engines named above. This isachieved by an automatic phrase recognition techniques (see Kelledy, F.,Smeaton, A. F. 1997. Automatic Phrase Recognition and Extraction fromText. Proceedings of the 19^(th) Annual BCS-IRSG Colloquium on IRResearch) using the rule that when precisely two perfect phrasescomprise the subject 220 and one of the phrases is a proper geographicalname (e.g. “New York City”) or a proper name (“Rialto Theatre”) and oneof the phrases is an adjective+noun phrase (“show times” or “Italianrestaurants”), the simple web search engine should be invoked. Moresophisticated rules can easily be defined to cover most circumstances.

The third input mode illustrated in FIG. 1 is more fully illustrated inFIG. 6 wherein the input to the correlation function 110 is a userquestion, and the user question shall be composed of an incompletequestion in canonical form and, in addition, one or more key words,wherein the key words complete the question [comparable to the wellknown paradigm of “fill in the blanks”]. Alternatively, the incompletequestion will be explicitly selected by the user. In one embodiment, theincomplete question will be explicitly selected by the user from a listor menu of supported canonical form questions. In another, the list ormenu of incomplete supported canonical form questions will be“static”—that is, the list will not vary at each invocation.Alternatively, the list or menu of incomplete supported canonical formquestions will be “dynamic”—that is, the list varies at each invocation.Referring to FIG. 1, the dynamic list or menu of incomplete supportedcanonical form questions will be generated at each invocation by meansof a software function, the canonical form question generation function250, a software program component, written in a computer programminglanguage (e.g. Java, a product of Sun Microsystems, Inc.).Alternatively, the incomplete question will be implicit, the questionbeing selected by a software program component, the canonical formquestion generation function 250. Or, the incomplete implicit questionthat will be selected by the canonical form question generation function250 will be “static”—that is, it will not vary at each invocation.

In a currently preferred embodiment, the static implicit selectedquestion is “What are the connections between [keyword 1] and [keyword2]?” Alternatively, the static implicit selected question is “What arethe connections between [keyword 1] and [keyword 2] in the context of[keyword 3] and/or [keyword 4] and/or [keyword 5]?” Or, the incompleteimplicit question that will be selected by the canonical form questiongeneration function 250 will be “dynamic”—that is, it will vary at eachinvocation.

In one embodiment, the digital information object 230 will be providedby a user. The digital information object 230 will include, but not belimited to the forms:

-   -   (i) text (plain text) files.    -   (ii) Rich Text Format (RTF) (a standard developed by Microsoft,        Inc.). An alternative method is to first obtain clean text from        RTF by the intermediate use of a RTF-to-text conversion utility        (e.g. RTF-Parser-1.09, a product of Pete Sergeant).    -   (iii) Extended Markup Language (XML) (a project of the World        Wide Web Consortium) files.    -   (iv) any dialect of markup language files, including, but not        limited to: HyperText Markup Language (HTML) and Extensible        HyperText Markup Language (XHTML™) (projects of the World Wide        Web Consortium), RuleML (a project of the RuleML Initiative),        Standard Generalized Markup Language (SGML) (an international        standard), and Extensible Stylesheet Language (XSL) (a project        of the World Wide Web Consortium).    -   (v) Portable Document Format (PDF) (a proprietary format of        Adobe, Inc.) files (by means of the intermediate use of a        PDF-to-text conversion utility).    -   (vi) MS WORD files e.g. DOC files used to store documents by MS        WORD (a word processing software product of Microsoft, Inc.)        This embodiment programmatically utilizes a MS Word-to-text        parser (e.g. the Apache POI project, a product of Apache.org).        The POI project API also permits programmatically invoked text        extraction from Microsoft Excel spreadsheet files (XLS). An MS        Word file can also be processed by a NLP as a plain text file        containing special characters, although XLS files cannot.    -   (vii) event-information capture log files, including, but not        limited to: transaction logs, telephone call records, employee        timesheets, and computer system event logs.    -   (viii) web pages    -   (ix) blog pages    -   (x) a relational database row.    -   (xi) a relational database view.    -   (xii) a relational database table.    -   (xiii) a relational database answer set (i.e. the set of rows        resulting from a relational algebra operation).

The topic of the digital information object 230 will be determined by asoftware function, the topic detection function 240, a software programcomponent. Examples of such topic detection software have been welldescribed in the literature (see Chen, K. 1995. Topic Identification inDiscourse. Morgan Kaufman). The topic detection function 240 will beimplemented with software adapters 235 that handle each form of digitalinformation object 230. Such software adapters 235 are well known (foran example, seehttp://www-306.ibm.com/software/integration/wbiadapters/framework). Theoutput of the topic detection function will be keywords and/or phraseswhich will then be submitted to the correlation function 110.

FIG. 8 is a flow chart of the search engine process initiated by theknowledge correlation function 110 upon inputs as described in FIG. 1,and continuing through to presentation of results to a user inaccordance with one aspect of the invention. The correlation function110 places relevant data structure objects 830, triples 835 andassociated objects 837 into an answer space 885. The significance of theobjects in the answer space 885 is determined by a significancecalculation function 840 which sets up data for the ranking function 845to rank by significance. Output is then displayed to the user. In theevent the correlation function creates any kind of directed acyclicgraph, the graph can be displayed to the user after being organized forlayout by the hierarchical layout function 850.

The present invention is dependent upon the success of the correlationfunction 110 disclosed in patent application Ser. No. 11/273,568, whichsummarizes the correlation function 110 used in the present invention.

Computers that may be searched in this way include individual personalcomputers, individual computers on a network, network server computers,network ontology server computers, network taxonomy server computers,network database server computers, network email server computers,network file server computers. Network ontology servers are specialtypically high performance computers which are dedicated to the task ofsupporting semantic search functions for a large group of users. Networktaxonomy servers are special typically high performance computers whichare dedicated to the task of supporting taxonomic search functions for alarge group of users. Network database servers are special typicallyhigh performance computers which are dedicated to the task of supportingdatabase functions for a large group of users. Network email servers arespecial typically high performance computers which are dedicated to thetask of supporting email functions for a large group of users. Networkfile servers are special typically high performance computers which arededicated to the task of supporting file persistence and retrievalfunctions for a large group of users. The computer network has a minimumof two network nodes and the maximum number of network nodes isinfinity. The computer file system has a minimum of two files and themaximum number of files is infinity.

Upon successful completion of the correlation function 110, an answerspace 800 will exist. As described in said Ser. No. 11/273,568, andillustrated in FIG. 8 of this application, the answer space 800 iscomposed of correlations (Ser. No. 11/278,568 Item 155). Thecorrelations (Ser. No. 11/278,568 Item 155) are in turn composed ofnodes FIG. 5 (Ser. No. 11/278,568 Items 180A and 180B). The successfulcorrelations FIG. 4 (Ser. No. 11/278,568 Item 155) produced by thecorrelation function 110 are together modeled as a directed graph (alsocalled a digraph) of correlations in one preferred embodiment.Alternatively, the successful correlations FIG. 4 (Ser. No. 11/278,568Item 155) produced by the correlation function 110 are together modeledas a quiver of paths of successful correlations. Successful correlationsFIG. 4 (Ser. No. 11/278,568 Item 155) produced by the correlationfunction 110 are together called, with respect to correlation, theanswer space 800. Where the correlation function 110 constructs a quiverof paths where each path in the quiver of paths is a successfulcorrelation, all successful correlations share as a starting point theorigin node (Ser. No. 11/278,568 Item 152), and all possiblecorrelations (Ser. No. 11/278,568 Item 155) from the origin node (Ser.No. 11/278,568 Item 152) are constructed. All correlations (Ser. No.11/278,568 Item 155) (paths) that start from the same origin term-node(Ser. No. 11/278,568 Item 152) and terminate with the same targetterm-node (Ser. No. 11/278,568 Item 159) or the same set of relatedtarget term-nodes (Ser. No. 11/278,568 Item 159) comprise a correlationset.

In a currently preferred embodiment, the answer space 800 is stored in acomputer digital memory, or stored on a computer digital storage media(e.g. a hard drive). Such digital memory and digital storage devices arewell known. The answer space 800 transiently resides or is persisted ona computing device, a computer network-connected device, or a personalcomputing device. Well known computing devices include, but are notlimited to super computers, mainframe computers, enterprise-classcomputers, servers, file servers, blade servers, web servers,departmental servers, and database servers. Well known computernetwork-connected devices include, but are not limited to internetgateway devices, data storage devices, home internet appliances, set-topboxes, and in-vehicle computing platforms. Well known personal computingdevices include, but are not limited to, desktop personal computers,laptop personal computers, personal digital assistants (PDAs), advanceddisplay cellular phones, advanced display pagers, and advanced displaytext messaging devices. The answer space 800 contains or associates aminimum of two nodes (Ser. No. 11/278,568 Items 180A and 180B) and themaximum number of nodes (Ser. No. 11/278,568 Items 180A and 180B) isinfinity.

Because the nodes (Ser. No. 11/278,568 Items 180A and 180B) are theproducts of a decomposition function (Ser. No. 11/278,568 Item 130)applied against the resources (Ser. No. 11/278,568 Item 128) identifiedby the Discovery phase of the correlation function 110, the nodes (Ser.No. 11/278,568 Items 180A and 180B) are strongly associated with theresources (Ser. No. 11/278,568 Item 128) from which the nodes (Ser. No.11/278,568 Items 180A and 180B) were derived. Such resources (Ser. No.11/278,568 Item 128) are here called contributing resources. Further,the answer space 800 is strongly associated with a user query(manifested as input subjects 200, digital information objects 230, orquestions 250) because a successful correlation (Ser. No. 11/278,568Item 155) is an existential proof (existential quantification) that theuser query can be satisfied from the contents of corpus. The presentinvention is based upon the fact that the strong association of the userquery to the answer space 800 is transitive to the resources (Ser. No.11/278,568 Items 128) which contributed nodes (Ser. No. 11/278,568 Items180A and 180B) to the answer space, thereby enabling the presentinvention of a knowledge correlation search engine to deliver highlyaccurate links of resources (Ser. No. 11/278,568 Items 128) which arerelevant to the user query.

A requirement of the present invention is that the resources (Ser. No.11/278,568 Item 128) which contributed nodes (Ser. No. 11/278,568 Items180A and 180B) to the answer space 185 must be identified (i.e what arethe contributing resources 000?). As can be seen in Ser. No. 11/278,568,Item 180B, a member of node Ser. No. 11/278,568 Item 180B is theSequence (source) (Ser. No. 11/278,568 Item 188). The sequence (Ser. No.11/278,568 Item 188) contains the URI of the resource (Ser. No.11/278,568 Item 128) from which the node (Ser. No. 11/278,568 Item 180B)was derived (the contributing resource 128 for that node Ser. No.11/278,568 Item 188). Therefore, the present invention can identifycontributing resources 128 which are relevant to the user query bysimply enumerating the URIs of all resources (Ser. No. 11/278,568 Item128) found in all nodes (Ser. No. 11/278,568 Item 188) in the answerspace 185.

In an improved, but still rudimentary embodiment, each correlation (Ser.No. 11/278,568 Item 155) can be examined, and the frequency ofoccurrence of a contributing resource 128 in the correlation (Ser. No.11/278,568 Item 155) can be captured in a histogram. The cumulativecounts for the occurrence of all contributing resources 128 can then besorted. The URIs for all contributing resources 000 can then bepresented to the user in order of descending frequency of occurrence.For this embodiment and referring to FIG. 2, the examination of thecorrelations (Ser. No. 11/278,568 Item 155), capture of frequency ofoccurrence of contributing resources 128, and the placement of thecaptured frequency of occurrence of contributing resources 128 into ahistogram is performed by a significance calculation function 540. Thesorting of the cumulative counts for the occurrence of all contributingresources 128 is performed by a ranking function 545, and thepresentation to the user of the sorted results is performed by ahierarchical layout function 550.

In another rudimentary example, the significance calculation function842 is a statistical function that is based upon the number of uniquenodes (Ser. No. 11/278,568 Item 180B) contributed to the answer space885 by each contributing resource 128. In this embodiment, anycorrelations (Ser. No. 11/278,568 Item 155) in the answer space 885 arenot considered. The significance calculation function 842 first liststhe unique nodes (Ser. No. 11/278,568 Item 180B) in the answer space885, with one entry in the list for each node (Ser. No. 11/278,568 Item180B). Then, the frequency of reference to each contributing resource128 is counted. Using standard and well-known statistical criteria andmethods to measure statistical significance, the k threshold to be usedby the ranking function 845 is established, and the most significantcontributing resources 128 can be identified and presented to the user.

For another example, the significance calculation function 842correlates the simple occurrence frequency to the simple contributionfrequency value, resulting in a rudimentary significance score. If ascatter plot were used to display this data, the significant resources128 with highest occurrence frequency and the highest contributionfrequency would place farthest to the right and closest to the top.Again, as for all the varied embodiments of the significance calculationfunction 842 described more hereinafter, standard and well knownstatistical significance measures are utilized to provide appropriate kthreshold information for the ranking function 845. Other statisticaltechniques that may be utilized by the significance calculation function842—as needed—include, but are not limited to: linear (the well knownPearson r) correlation between the frequency of occurrence and simplecontribution; non-linear correlations of the plot data; nonparametricstatistical approaches such the Kendall coefficient of concordance,computation of the geometric mean for data which have logarithmicrelation to each other, and other well known techniques to measure therelationship between the variables.

In one embodiment, a node significance score can be computed by usingmeasures such as the ratio, frequency of occurrence over number of nodes(Ser. No. 11/278,568 Item 180B) contributed by that specific node's(Ser. No. 11/278,568 Item 180B) contributing resource 128, or the ratio,frequency of occurrence over the average number of nodes (Ser. No.11/278,568 Item 180B) contributed by all contributing resources 128. Toimprove the speed of the significance calculation function 842, nodesignificance scores can be normalized (0,1) or (−1,1), with thepossibility thereby to rapidly determine if a given contributingresource 128 was significant or not significant to the answer space.

In another, the significance calculation function 842 is a link analysisfunction, the link analysis function 842 taking the correlation (Ser.No. 11/278,568 Item 155) as input. This exploits the differences betweenthe correlation (Ser. No. 11/278,568 Item 155) created by thecorrelation function 110 compared to a web graph. The significancecalculation function 842 as link analysis function establishes a linkpopularity score on each of node (Ser. No. 11/278,568 Item 180B) in theanswer space 128. The link popularity score is determined by means ofthe number of in-degree links to each node (Ser. No. 11/278,568 Item180B) in the answer space 885. The popularity score values of all nodes(Ser. No. 11/278,568 Item 180B) contributed by a contributing resource128 are then summed. In this embodiment, the aggregate popularity scoresof all nodes (Ser. No. 11/278,568 Item 180B) contributed by acontributing resource 128 are transit to the contributing resource 128itself.

In one embodiment, the significance calculation function 842 as linkanalysis function establishes an importance score on each of the nodes(Ser. No. 11/278,568 Item 180B). The importance score is determined bymeans of the well known Kleinberg Hubs and Authorities algorithm. Hub orAuthority scores for all nodes (Ser. No. 11/278,568 Item 180B)contributed by a contributing resource 128 are then summed. In thisembodiment, the aggregate Hub and Authority scores of all nodes (Ser.No. 11/278,568 Item 180B) contributed by a contributing resource 128 aretransit to the contributing resource 128. In an embodiment, theimportance score is determined by means of the well-known 2nd version ofthe PageRank algorithm. PageRank scores for all nodes (Ser. No.11/278,568 Item 180B) contributed by a contributing resource 128 arethen summed. In this embodiment, the aggregate PageRank scores of allnodes (Ser. No. 11/278,568 Item 180B) contributed by a contributingresource 128 are transit to the contributing resource 128.

The results of resource significance calculation function 842 will beranked by means of a software function, the ranking function 845, asoftware program component. In an embodiment, the ranking function 845implements a simple descending sort, with the contributing resource 128given the highest value by the significance calculation function 842awarded the number one rank by the ranking function 845, and the ordinalrank of the other contributing resources 128 being assigned based upontheir relative position in the sorted list of significance values. Whenthe significance calculation function 842 is a statistical function thatis based upon the number of discrete nodes (Ser. No. 11/278,568 Item180B) contributed to the answer space 885 by each contributing resource128, and when the ranking function 845 implements a simple descendingsort, the ranking function is called rank by contribution. When thesignificance calculation function 842 is a statistical function thatwill calculate the sum of the relevance scores for all nodes (Ser. No.11/278,568 Item 180B) contributed to the answer space 885 by eachcontributing resource 128, and when the ranking function 845 implementsa simple descending sort, the ranking function is called rank byrelevance. When the significance calculation function 842 is astatistical function that will calculate the sum of the popularityscores, Hub and Authority scores, or PageRank scores for all nodes (Ser.No. 11/278,568 Item 180B) contributed to the answer space 885 by eachcontributing resource 128, and when the ranking function 845 implementsa simple descending sort, the ranking function is called rank bysignificance.

In a currently preferred embodiment, at least two categories ofcontributing resources 128 contribute nodes (Ser. No. 11/278,568 Item180B) to the answer space 885. The two categories of contributingresources are here designated topical resources, and referenceresources. Topical resources provide nodes (Ser. No. 11/278,568 Item180B) with explicit reference to a topic, for example the triple GLOBALWARMING-AFFECTS-GLACIERS. Reference resources provide nodes (Ser. No.11/278,568 Item 180B) which anchor the foundations in knowledge thatsupport topical resource nodes (Ser. No. 11/278,568 Item 180B), forexample the triple GLOBAL WARMING-IS-CLIMATE CHANGE, or GLOBALWARMING-FROM-EMISSIONS.

In an embodiment, a Support Vector Machine (SVM) is created to classifyand rank contributing resources. Depending upon the characteristics andnumber of contributing resource 125 classifications, variations of SVMare utilized, including SVM-RFE (SVM-Recursive Feature Elimination), andR-SVM (Reduced-SVM). In a currently preferred embodiment, where theclassification of contributing resources 128 into topical resources andreference resources is required, the application of SVM procedures toclassify and rank the contributing resources 128 is essentiallyidentical the SVM procedure used as a diagnostic classifier to identifyhealthy tissue samples from cancer tissue samples.

In a currently preferred embodiment, the correlation (Ser. No.11/278,568 Item 155) constructed or discovered by the correlationfunction 110 can be displayed to a user. This display is called apresentation. In a currently preferred embodiment, the presentation ofthe answer space 128 will be implemented using a hierarchical layout890. In a currently preferred embodiment, the hierarchical layout 890will be created using a software function, the hierarchical layoutfunction 850, a software program component. The hierarchical layoutfunction 850 assigns the nodes of graphs on different layers in such away that most edges in the graph flow in the same direction and thenumber of intersecting edges are minimized. In a currently preferredembodiment, hierarchical layout function 850 uses the Sugiyama-layoutalgorithm.

While various embodiments of the present invention have been illustratedherein in detail, it should be apparent that modifications andadaptations to those embodiments may occur to those skilled in the artwithout departing from the scope of the present invention as set forthin the following claims.

What is claimed is:
 1. A computer implemented method for displaying aranked plurality of resources based upon at least one input, the methodcomprising: performing at least one input evaluation function forgenerating a plurality of extracted inputs from the at least one input;decomposing a plurality of resources into a plurality of nodes;generating an answer space by performing at least one knowledgecorrelation function on a node pool based upon the plurality ofextracted inputs, each node of the node pool comprising a data structureincluding a subject, an attribute and a bond therebetween, the knowledgecorrelation function comprising iteratively adding nodes from the nodepool onto an end of a chain of nodes by searching the node pool for amatch between an attribute of a chained node and a subject of anotherunchained node in the node pool; determining a plurality of mostsignificant resources based upon the answer space; ranking insignificance the plurality of most significant resources to therebygenerate the ranked plurality of resources; and displaying the rankedplurality of resources.
 2. The method according to claim 1 whereinperforming the at least one input evaluation function comprisesperforming a subject evaluation function for extracting subjectinformation from at least one of keywords; phrases; sentences; concepts;compound, complex and orthogonal inputs; and a simple web query.
 3. Themethod according to claim 2 wherein performing the subject evaluationfunction for at least one of keywords and phrases comprises performing apass through function.
 4. The method according to claim 2 whereinperforming the subject evaluation function for sentences comprisesperforming a natural language parser function.
 5. The method accordingto claim 2 wherein performing the subject evaluation function forconcepts comprises performing an evaluation for at least one of subject,object and context information.
 6. The method according to claim 2wherein performing the subject evaluation function for at least one ofcompound, and complex and orthogonal inputs comprises performing aclause recognition function and a natural language parser function. 7.The method according to claim 2 wherein performing the subjectevaluation function for a simple web query comprises performing a phraserecognition function.
 8. The method according to claim 1 wherein theinput comprises a digital information object; and wherein performing theat least one user input evaluation comprises using a topic detectionmodule adapter and a topic detection module downstream therefrom.
 9. Themethod according to claim 8 wherein performing the at least one userinput evaluation further comprises using a natural language parserdownstream from the topic detection module.
 10. The method according toclaim 1 further comprising performing a question generating function toobtain the at least one input for defining a query.
 11. The methodaccording to claim 1 wherein the most significant resources aretransitively associated with the at least one input through resourcesused to create the answer space.
 12. The method according to claim 1wherein determining the plurality of most significant resources does notuse a similarity measure.
 13. A physical non-transitorycomputer-readable medium having instructions stored thereon which, whenexecuted by a computer, cause the computer to perform an informationretrieval method comprising: performing at least one input evaluationfunction for generating a plurality of extracted inputs from at leastone input; decomposing a plurality of resources into a plurality ofnodes; generating an answer space by performing at least one knowledgecorrelation function on a node pool based upon the plurality ofextracted inputs, each node of the node pool comprising a data structureincluding a subject, an attribute and a bond therebetween, the knowledgecorrelation function comprising iteratively adding nodes from the nodepool onto an end of a chain of nodes by searching the node pool for amatch between an attribute of a chained node and a subject of anotherunchained node in the node pool; determining a plurality of mostsignificant resources based upon the answer space; ranking insignificance the plurality of most significant resources to therebygenerate the ranked plurality of resources; and displaying the rankedplurality of resources.
 14. The physical non-transitorycomputer-readable medium according to claim 13 wherein performing the atleast one input evaluation function comprises performing a subjectevaluation function for extracting subject information from at least oneof keywords; phrases; sentences; concepts; compound, complex ororthogonal inputs; and a simple web query.
 15. The physicalnon-transitory computer-readable medium according to claim 14 whereinperforming the subject evaluation function for at least one of keywordsand phrases comprises performing a pass through function.
 16. Thephysical non-transitory computer-readable medium according to claim 14wherein performing the subject evaluation function for sentencescomprises performing a natural language parser function.
 17. Thephysical non-transitory computer-readable medium according to claim 14wherein performing the subject evaluation function for conceptscomprises performing an evaluation for at least one of subject, objectand context information.
 18. The physical non-transitorycomputer-readable medium according to claim 14 wherein performing thesubject evaluation function for at least one of compound, complex andorthogonal inputs comprises performing a clause recognition function anda natural language parser function.
 19. The physical non-transitorycomputer-readable medium according to claim 14 wherein performing thesubject evaluation function for a simple web query comprises performinga phrase recognition function.
 20. The physical non-transitorycomputer-readable medium according to claim 13 further comprisingperforming a question generating function to obtain the at least oneinput for defining a query.